Futoshi Asano

An optimum computer‐generated pulse signal suitable for the measurement of very long impulse responses

Transfer functions of acoustic systems often exhibit wide dynamic ranges and very long impulse responses. A ‘‘time‐stretched’’ pulse as proposed by Aoshima (ATSP), though originally given in a very specific form seems to be one of the most promising signals to measure transfer functions with characteristics of acoustic system mentioned as above. In this paper, this pulse (ATSP) is first generalized and then optimized for the measurement of long impulse responses. This optimized ATSP (OATSP) has an almost ideal characteristic to measure impulse responses shorter than its specific length N. Moreover, it is newly shown in this paper that OATSP has also a good characteristic to measure impulse responses longer than N. Discussion is presented on how to design OATSP suitable for a specific situation of measurement by analyzing errors, when the pulse is used to measure impulse responses longer than N.

Role of spectral cues in median plane localization

The role of spectral cues in the sound source to ear transfer function in median plane sound localization is investigated in this paper. At first, transfer functions were measured and analyzed. Then, these transfer functions were used in experiments where sounds from a source on the median plane were simulated and presented to subjects through headphones. In these simulation experiments, the transfer functions were smoothed by ARMA models with different degrees of simplification to investigate the role of microscopic and macroscopic patterns in the transfer functions for median plane localization. The results of the study are summarized as follows: (1) For front-rear judgment, information derived from microscopic peaks and dips in the low-frequency region (below 2 kHz) and the macroscopic patterns in the high-frequency region seems to be utilized; (2) for judgment of elevation angle, major cues exist in the high-frequency region above 5 kHz. The information in macroscopic patterns is utilized instead of that in small peaks and dips.

Combined approach of array processing and independent component analysis for blind separation of acoustic signals

Two array signal processing techniques are combined with independent component analysis (ICA) to enhance the performance of blind separation of acoustic signals in a reflective environment. The first technique is the subspace method which reduces the effect of room reflection when the system is used in a room. Room reflection is one of the biggest problems in blind source separation (BSS) in acoustic environments. The second technique is a method of solving permutation. For employing the subspace method, ICA must be used in the frequency domain, and precise permutation is necessary for all frequencies. In this method, a physical property of the mixing matrix, i.e., the coherency in adjacent frequencies, is utilized to solve the permutation. The experiments in a meeting room showed that the subspace method improved the rate of automatic speech recognition from 50% to 68% and that the method of solving permutation achieves performance that closely approaches that of the correct permutation, differing by only 4% in recognition rate.

Robust speech interface based on audio and video information fusion for humanoid HRP-2

For cooperative work of robots and humans in the real world, a communicative function based on speech is indispensable for robots. To realize such a function in a noisy real environment, it is essential that robots be able to extract target speech spoken by humans from a mixture of sounds by their own resources. We have developed a method of detecting and extracting speech events based on the fusion of audio and video information. In this method, audio information (sound localization using a microphone array) and video information (human tracking using a camera) are fused by a Bayesian network to enable the detection of speech events. The information of detected speech events is then utilized in sound separation using adaptive beam forming. In this paper, some basic investigations for applying the above system to the humanoid robot HRP-2 are reported. Input devices, namely a microphone array and a camera, were mounted on the head of HRP-2, and acoustic characteristics for sound localization/separation performance were investigated. Also, the human tracking system was improved so that it can be used in a dynamic situation. Finally, overall performance of the system was tested via off-line experiments.

Jijo-2: an office robot that communicates and learns

Describes how the authors have combined speech recognition, dialogue management, and statistical learning procedures to develop Jijo-2; an office robot that can communicate with humans and learn about its environment.

Speech enhancement based on the subspace method

A method of speech enhancement using microphone-array signal processing based on the subspace method is proposed and evaluated. The method consists of the following two stages corresponding to the different types of noise. In the first stage, less-directional ambient noise is reduced by eliminating the noise-dominant subspace. It is realized by weighting the eigenvalues of the spatial correlation matrix. This is based on the fact that the energy of less-directional noise spreads over all eigenvalues while that of directional components is concentrated on a few dominant eigenvalues. In the second stage, the spectrum of the target source is extracted from the mixture of spectra of the multiple directional components remaining in the modified spatial correlation matrix by using a minimum variance beamformer. Finally, the proposed method is evaluated in both a simulated model environment and a real environment.

Intelligent sound source localization for dynamic environments

As robotic technology plays an increasing role in human lives, “robot audition”, human-robot communication, is of great interest, and robot audition needs to be robust and adaptable for dynamic environments. This paper addresses sound source localization working in dynamic environments for robots. Previously, noise robustness and dynamic localized sound selection have been enormous issues for practical use. To correct the issues, a new localization system “Selective Attention System” is proposed. The system has four new functions: localization with Generalized EigenValue Decomposition of correlation matrices for noise robustness(“Localization with GEVD”), sound source cancellation and focus (“Target Source Selection”), human-like dynamic Focus of Attention (“Dynamic FoA”), and correlation matrix estimation for robotic head rotation (“Correlation Matrix Estimation”). All are achieved by the dynamic design of correlation matrices. The system is implemented into a humanoid robot, and the experimental validation is successfully verified even when the robot microphones move dynamically.

Adaptive feedback cancellation with frequency compression for hearing aids

The use of an adaptive feedback canceler (AFC) for howling suppression in hearing aids seems very attractive since it is not only unaffected by the changes in the operating environment, but it also limits signal degradation due to the feedback signal. This, however, requires a reference signal which is correlated with the feedback signal but not with the input signal. In hearing aids, such a signal is hard to obtain. The output signal could be used as reference if its correlation with the input signal could sufficiently be removed. If the reference signal is correlated with the input signal, the input signal will also be canceled by the AFC. Here, the use of a frequency compressor as a decorrelator is proposed. The performance of this system is then investigated via digital simulation. Results indicated that with the use of the proposed system and the proper choice of system parameters, an increase of about 18 dB in the howling margin could be achieved with minimal deterioration in output signal quality.

A combined approach of array processing and independent component analysis for blind separation of acoustic signals

Two array signal processing techniques are combined with independent component analysis to enhance the performance of blind separation of acoustic signals in a reflective environment such as rooms. The first technique is the subspace method which reduces the effect of room reflection. The second technique is a method of solving the permutation, in which the coherency of the mixing matrix in adjacent frequencies is utilized.

Detection and separation of speech event using audio and video information fusion and its application to robust speech interface

A method of detecting speech events in a multiple-sound-source condition using audio and video information is proposed. For detecting speech events, sound localization using a microphone array and human tracking by stereo vision is combined by a Bayesian network. From the inference results of the Bayesian network, information on the time and location of speech events can be known. The information on the detected speech events is then utilized in the robust speech interface. A maximum likelihood adaptive beamformer is employed as a preprocessor of the speech recognizer to separate the speech signal from environmental noise. The coefficients of the beamformer are kept updated based on the information of the speech events. The information on the speech events is also used by the speech recognizer for extracting the speech segment.